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<p>This paper focuses on studying fixed effects partially linear single index spatial error model with correlated structure within individuals. By combining spline basis function approximation, variable transformation, penalty function and quadratic inference function, penalized quadratic inference functions estimators for unknowns are constructed. By appropriately selecting tuning parameters, we derive that the parametric estimators satisfy consistency and asymptotic normality, and the nonparametric estimator has the optimal convergence rate. Numerical simulation implies the estimates have excellent small sample performance. The proposed model is applied to analyze the driving forces of China’s provincial digital economy development.</p>

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Penalized quadratic inference functions estimation for fixed effects partially linear single index spatial error model

  • 期刊论文

  • 发表日期：

    2025年5月25日

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  • Fen Li,
  • Hao Chen

摘要

This paper focuses on studying fixed effects partially linear single index spatial error model with correlated structure within individuals. By combining spline basis function approximation, variable transformation, penalty function and quadratic inference function, penalized quadratic inference functions estimators for unknowns are constructed. By appropriately selecting tuning parameters, we derive that the parametric estimators satisfy consistency and asymptotic normality, and the nonparametric estimator has the optimal convergence rate. Numerical simulation implies the estimates have excellent small sample performance. The proposed model is applied to analyze the driving forces of China’s provincial digital economy development.